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DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
De novo synthesis of 2,2-bis(dimethylamino)-3-alkyl or benzyl 2,3-dihydroquinazolin-4(1H)-one compounds
A new versatile and efficient strategy for the synthesis of 2,2-bis(dimethylamino)-3-alkyl or benzyl 2,3-dihydroquinazoline-4(1H)-one compounds has been developed by one-pot multicomponent reaction with isatoic anhydride, amines followed by in situ-generated Vilsmeier reagent. The reaction has also been studied with different amines and solvents. 2017 Taylor & Francis. -
Dealing with missing values in a relation dataset using the DROPNA function in python
Python provides a rich data structure library called PANDAS, which provides fast and efficient data transformation and analysis. The word PANDAS is an abbreviation of Python Data Analysis Library. PANDAS facilitate optimized and dynamic data structure designs work with "relational" or "labeled" data. Python's approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. PANDAS Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. [1]. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python's future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language's design and features [2]. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using PANDAS library. 2023 Scrivener Publishing LLC. -
Death of Vernaculars and Language Hegemony: An ethnography of the higher education sector in 21st century India
The paper examines how new age pedagogies and neoliberal policies consciously work towards naturalizing English languages hegemony in institutions of Higher Education (IHE) in India. An ethnographic study the paper foregrounds the precarious positioning of non-English Indian languages vis-vis the pervading discourses of internationalization and education as job/skill oriented. Hegemony of English in the present is coupled with a restructuring of language departments as well as fleeting market demands for human capital. The paper also brings into question the role of the Internet and related technologies in reorganizing the linguistic dynamics of HE. Instead of democratizing, the Internet produces new monopolies in knowledge production, controls knowledge traffic from global North to South and further legitimizes the language hegemony. The paper argues that, in the last two decades, the neoliberal rupture has been leading HE institutions to a death of vernaculars within their physical, cultural and academic spaces. 2024, Hiroshima University,Research Institute for Higher Education,. All rights reserved. -
Death Rituals and Change Among Hindu Nadars in a South Indian Village
This article examines changes in the death rituals performed among Hindu Nadars in a South Indian village. It emphasises the importance of understanding ritual changes within their specific micro-level local contextual framework, including changing social structures at household and village level. This empirical evidence showcases how changing rituals connected to death reflect various adaptations through imitation, substitution and alteration of specific ritual elements and performants. It also identifies emerging class distinctions among Nadars and their connection with changes in rituals associated with death. This analysis of the changes depicts how Nadars use ritual actions in pragmatic ways, symbolically expressing and realising their aspirations for status enhancement through such ritual performances. 2021 SAGE Publications. -
Death-worlds, Necropolitics and Decoloniality Colonial Negotiations in Mah
The boundaries of sovereignty are mostly relegated to modern and late modern political thoughts that focus on biopolitical and democratic theories. This paper marks a shift of sovereign subjectivity to the interstitial spaces of life and death of the colonial subjects. Through the study of the necropolitics of colonial control in the erstwhile French colony of Mah as narrated in the novel On the Banks of the Mayyazhi, this paper argues that colonial subjectivity and the idea of sovereignty have decentred itself from the traditional notions of political control and violence to newer avenues of life and death. The perusal of the decolonial approach to necropolitics will examine how colonial logic has shaped the idea of sovereignty. 2024 Economic and Political Weekly. All rights reserved. -
Decent Work Deficit: A Challenge on the Women Empowerment in Indian Agricultural Sector
Women play a crucial role in Indian agriculture, but they also confront several obstacles that reduce their productivity and prevent them from fully engaging in the sectors development. The majority of women in India are employed in agriculture, which is one of the sectors that contributes most to the GDP and is essential to the economic development of the nation. Although women continue to have a significant and recognized role in agriculture, their function is frequently overlooked. Women make up about 75% of the full-time labor force on Indian farms. The nation wont develop unless its women farmers are empowered. Only through decent work labour the agriculture sector will be developed which will help in the empowermentof women agricultural Labourers in India. So the government should take all steps to implement the decent work concept of ILO in the Indian agricultural sector. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Deciphering the global research trends and significance of moral intelligence via bibliometric analysis
Introduction: Moral Intelligence (MI) as a concept has gained importance in recent years due to its wide applicability in individual, organizational, and clinical settings or even policy making. The present study employed Bibliometric analysis to understand the emerging topics associated with MI and its global research trend. This papers primary aim was (i) to explore the temporal and geographic growth trends of the research publication on MI. (ii) to identify the most prolific countries, institutions, and authors, working on MI, (iii) to identify the most frequent terminologies, (iv) to explore research topics and to provide insight into potential collaborations and future directions, and (v) to explore the significance of the concept of moral intelligence. Method: Bibliometric analysis was used to understand the emerging topics associated with MI and its global research trend using the SCOPUS database. VOS viewer and R were employed to analyze the result. Through the analysis conducted, the development of the construct over time was analyzed. Results: Results have shown that Iran and the United States and these two combined account for 53.16% of the total country-wise publications. Switzerland has the highest number of Multi-county publications. Authors from Iran and Switzerland have the most number of publications. Emerging topics like decision-making, machine ethics, moral agents, artificial ethics, co-evolution of human and artificial moral agents, green purchase intention etc were identified. Discussion: The application of MI in organisational decision-making, education policy, artificial intelligence and measurement of moral intelligence are important areas of application as per the results. Research interest in MI is projected to increase according to the results delineated in this article. Copyright 2024 Bagchi, Srivastava and Tushir. -
Deciphering the impact of COVID-19 pandemic on food security, agriculture, and livelihoods: A review of the evidence from developing countries
With COVID-19 now spreading in developing countries, massive consequences on health and livelihoods are feared. Food security is the most important and crucial aspect of sustainable development. The agricultural sector forms the backbone of the economy and provides livelihood to a large section in developing countries. Therefore, the disruption in food security and the agricultural sector will have far-reaching impacts on these countries. Owing to the importance of these sectors, this paper performs a comprehensive assessment of the effect of COVID-19 on food security and agriculture. The research suggests coping and mitigation mechanisms that can be adopted to sustain livelihoods. 2020 The Author(s) -
Deciphering the Nature and Dynamics of Gig-Platform Jobs: Workers Hidden Precarity
The technology-driven gig-platform sector has emerged as a new source of employment generation both globally as well domestically. This recent transformation in the labour market is reshaping the nature of labour practices, labour relations, workers rights, and contracts. The sector has huge potential to generate millions of job opportunities by leveraging the use of digital technology. As this sector continues to generate more jobs, such jobs are portrayed as fostering economic growth, while creating meaningful jobs, which are mutually beneficial to workers and employers in terms of providing flexibility and freedom, better earning opportunity, and promoting social inclusion, by which it implies that women are increasingly equipped to find better jobs. This article critically examines the developmental roles of platform jobs which are being particularly highlighted within the policy circle, in academic literature, and tech companies through workers lens. It delves deeper into the discussion on those very aspects of platform jobs just listed, including the flexibility and freedom debate, workers income, and the gender aspect of jobs. In doing so, it carefully examines these aspects with respect to their implications on workers in terms of working conditions and regulatory aspects. The article brings out the workers precarity hidden within those developmental aspects of gig-platform jobs. 2024 CSD. -
Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model
Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government sizes estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels. The Author(s) 2024. -
Deciphering the properties of UV upturn galaxies in the Virgo cluster
The UV upturn refers to the increase in UV flux at wavelengths shorter than 3000 observed in quiescent early-type galaxies (ETGs), which still remains a puzzle. In this study, we aim to identify ETGs showing the UV upturn phenomenon within the Virgo galaxy cluster. We utilized a colourcolour diagram to identify all potential possible UV upturn galaxies. The spectral energy distributions (SED) of these galaxies were then analysed using the CIGALE software; we confirmed the presence of UV upturn in galaxies within the Virgo cluster. We found that the SED fitting method is the best tool to visualize and confirm the UV upturn phenomenon in ETGs. Our findings reveal that the population distributions regarding stellar mass and star formation rate properties are similar between UV upturn and red sequence galaxies. We suggest that the UV contribution originates from old stellar populations and can be modelled effectively without a burst model. Moreover, by estimating the temperature of the stellar population responsible for the UV emission, we determined it to be 13 000 K to 18 000 K. These temperature estimates support the notion that the UV upturn likely arises from the contribution of low mass evolved stellar populations (extreme horizontal branch stars). Furthermore, the Mg2 index, a metallicity indicator, in the confirmed upturn galaxies shows higher strength and follows a similar trend to previous studies. This study sheds light on the nature of UV upturn galaxies within the Virgo cluster and provides evidence that low-mass evolved stellar populations are the possible mechanisms driving the UV upturn phenomenon. 2024 The Author(s). -
Decision making framework for foreign direct investment: Analytic hierarchy process and weighted aggregated sum product assessment integrated approach
Foreign direct investment (FDI) plays a paramount role in economic and social growth of every country. FDI acts as a source of external capital and helps in economic growth of the host country. Making decision for FDI during uncertain business environment is a challenge for all stakeholders. Therefore, in this study, we are proposing a decision making framework for FDI. Through literature review, we have identified the factors, on which FDI depends. A process-based, multi-criterion, integrated hierarchical approach for deciding about FDI, has been illustrated. In this study, five sectors are considered, that is, petroleum and natural resource, retailing and e-commerce, healthcare, information technology, and road and highways for illustrating the proposed framework. It is observed that information technology sector has got top priority for FDI followed by retailing and e-commerce and health care sector. Findings will help in taking appropriate decision by stakeholders for FDI. Ultimately it will also help in creating employment, economic growth, and welfare of society at large in the host country. 2021 John Wiley & Sons, Ltd. -
Decision Tree Based Routing Protocol (DTRP) for Reliable Path in MANET
In mobile ad hoc network due to node movements, there exists route failure in active data transmission which results in data loss and communication overheads. Hence, in such a dynamic network, routing through reliable path is one of the tedious tasks. In this paper, we propose a novel Decision Tree based Routing Protocol (DTRP) a data mining technique in route selection process from source to destination. The proposed DTRP protocol selects the one hop neighbors based on the parameters such as speed, Link Expiration Time, trip_time and node life time. Thus the performance of a route discovery mechanism is enhanced by selecting the stable one-hop neighbors along the path to reach the destination. The simulated results show that the lifetime of the route is increased and hence the data loss and end to end delay are minimized thereby increasing the throughput of the network using the proposed DTRP routing protocol compared to existing routing protocols. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Decision-making using regression analysis: a case study on Top Tier Holidays LLP
Research methodology: This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study. Case overview/synopsis: This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing. Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment. Complexity academic level: This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes. 2023, Emerald Publishing Limited. -
Decoding Big Data: The Essential Elements Shaping Business Intelligence
In today's Business Intelligence (BI) world, Big Data Analytics integration has become critical, transforming company strategy and decision-making processes. This study investigates the complex influence of Big Data on business intelligence, focusing on important drivers of this transition. It investigates how Big Data's improved data processing capabilities, integration of advanced analytics techniques such as machine learning, and real-time data insights enable businesses to make more informed decisions and achieve a competitive advantage. Furthermore, the paper emphasizes the importance of personalized consumer insights, operational savings, and strategic benefits obtained from predictive analytics when adopting Big Data for BI. 2024 IEEE. -
Decoding boomerang hiring: A suggestive framework to improve organizational efficiency
In an ever changing, volatile and dynamic business environment, efforts put by the human resources reflect the organizational efficiency. Organizations should always focus on maintaining smooth relations with the Alumni and Boomerangs as they play a crucial role in the expanding horizons of business. A positive word of mouth also helps in improving the goodwill and image of the company. It will encourage the prospective employees to view the organization in a positive light. Rehiring former employees is one of the mechanisms for recruitment used by a large number of corporations primarily because of the inherent advantage of added experience as well as savings in terms of cost of recruitment and training. The present study attempts to give an overview of Boomerang Hiring, the possible value additions being made in terms of Human Capital and Social Capital on basis of the type of respective organizations they are returning from. Additionally, the perspective of the rehired employee is also presented. The study is further enriched by quoting a few instances from the corporate world. The Rehiring Strategies tailored as per organizational requirements will lead towards holistic growth and development of the entity. 2020 SERSC. -
Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024. -
Decoding Customer Lifetime Value to Unlock Business Success with Predictive Machine Learning Approach
This study highlights how crucial customers are for a company's success who directly impacts revenue and overall business value. This study focuses on analysis of customer lifetime value, the research uses data from 5000 customers with 8 important features with the main goal of predicting customer lifetime value. Business leaders often face choices about where to invest in marketing, like loyalty programs, incentives and ads or nothing. The study suggests that customer lifetime value is a key metric for making smart decisions, which measures how much a customer spends over their time with a company. To predict this value, the research explored different machine learning models - linear regression, decision tree regressor, random forest, and AutoML regressor. Each model is checked for how well it predicts customer spending habits. The results show that AutoML regression stands out for its accuracy without overcomplicating things. This study offers insights for businesses looking to improve their customer-focused strategies and long-term success. 2024 IEEE.